10/04/2011

The Firebird Project invites all Firebird users and developers to participate in the Firebird Conference 2011, hich will take place in Luxembourg, November 25-26, 2011.

IBSurgeon is a sponsor of Firebird Conference 2011, and we recommend all our customers to go there.

The Firebird Conference 2011 will be devoted to a variety of important topics: Firebird SQL language improvements, performance, optimization, database protection and recovery, and many others.

A number of presentations will be done by the Firebird Core team, which will ensure access to information at the best technical levels. The Firebird Conference 2011 will be a place to improve your Firebird development skills and give a chance to ask questions directly to Firebird Core team.

Firebird Core Team will be represented by Dmitry Yemanov, Vlad Khorsun, Alex Peshkoff, Philippe Makowski, etc. There are also will be speakers from IBSurgeon and other Firebird-related companies.

9/30/2011

Firebird Project is happy to announce general availability of Firebird 2.5.1 - a minor release of the award-winning relational database management system. This sub-release introduces several bug fixes and many important improvements - for example, performance improvements during a database restore, the ability to write to global temporary tables in read-only databases, etc.

For the full list of changes please refer to the Release Notes, Chapter 2 "New in Firebird 2.5".

Firebird 2.5.1 has 100% compatible on-disk structure with Firebird 2.5.0, so it is recommended to migrate to 2.5.1 as soon as possible. To download Firebird 2.5.1, please visit the download section at the official web-site: http://firebirdsql.org/en/firebird-2-5-1/

8/25/2011

As you probably know, manual sweep (by invoking gfix -sweep) is the important part of Firebird database maintenance (especially for big databases). Unfortunately, there are few people who understand the internals of sweep process. In this post we will not explain the magic of sweep, because it requires long and detailed explanation, instead of this we will provide you with the simple method to check that sweep was completed successfully and fulfilled its task.
After running gfix -sweep you need to run any Firebird client (like isql.exe) and commit at least one transaction - this is necessary to move other transactions' markers after sweep.
Then run gstat -h and check its output: all transaction markers should be aligned (i.e., with minimal gap):

If you see that gap is more than several transactions, it means that sweep did not remove all possible garbage (and in this case you can see gap of hundreds/thousands transactions). Unfortunately, sweep does not produce any errors or messages in firebird.log, so it's hard to determine the reason of failure.Also, the big gap is an obvious alert to check database statistics (produced by gstat -r, visualized by IBAnalyst).
The most often reason why sweep does not clear all record versions is long-running writeable transaction (and this is the most often reason why automatic sweep does not work well), but there are other unpleasant options, like database corruption.
If you are sure that there were no connected users during sweep, or you saw that sweep finished unusually quickly for the big database (like several second for database 5+Gb in size), consider it as an alert, and run validation (gfix -v -full) as soon as possible.

Disadvantage of such approach (in terms of recognizing problem in system area of Firebird with failed sweep) is that sweep usually scheduled to run once per day, and it requires explicit attention of administrator, because only indirect signs appear. To monitor database health around the clock we are using our FBDataGuard tool - it check the same metadata that sweep touches during sweeping, and it sends alert immediately if something is wrong.

8/23/2011

Right now at www.firebirdnew.org you can see the survey about Firebird features in production environment.

Some answers look like dangerous signals, especially for those who run big Firebird databases.

So, what's wrong with these answers?

1. CPUAffinity. CPUAffinity can be used to bind Firebird SuperServer to some particular CPU/core. At present we have plenty of cores even at desktop workstations, so there is no reason to use SuperServer and limit Firebird to the only CPU - use SuperClassic or Classic architectures to run Firebird at full throttle.

2. NBackup. In IBSurgeon we do not recommend our clients to use NBackup without external monitoring and as the only way of backup. NBackup makes the straight copy of database on page level - it's fast, but, unlike the gbak, it does not check pages' contents. If you use only NBackup and does not perform necessary database maintenance (at least regular sweep, combined with transaction markers monitoring), and someday your database became corrupted (due to RAM problem or abnormal shutdown, for example) , NBackup will continue to make (and overwrite) "backups". Another danger is "frozen: delta-file, when database are not unlocked correctly, and all changes are written to delta-file, and cannot be merged due to delta file problem. To use nbackup strengths and avoid pitfalls, we setup special backup scheme for our clients.

3. Shadow is completely useless in modern production environments. It provides protection from the only type of corruption - occasional critical crash of HDD (assuming that shadow is configured correctly, with 2 separate HDDs). Use RAID5 (or RAID10) instead - it will be much faster and more convenient for maintenance.

4. Multifile database. There is no actual reason to use multi-file database at present. With multi-file it will be impossible to do NBackup, database files will be tied to its locations, and it will give no advantage at all. Often multi-file databases are being implemented to perform copying of database to DVD, but you need to switch off Firebird during this operation.

Summary

Of course, the title of this post is a provocation, and sometimes administrator need to use CPUAffinity, nbackup and other features (not shadow!), in order to achieve some specific result, but it should be done correctly and with full understanding of steps to be done. NBackup is the most useful tool, which is very often understimated both in terms of strengths and possible problems with it.

7/17/2011

In the first part of our optimization story ("How to ruin Firebird performance...") we mentioned that indices should be analyzed before dropping, even if they appeared to be very bad in IBAnalyst in terms of garbage and number of unique values.

In essence, we need to log all queries used in applications, get their execution plans and check, how indices are used.

For this we need to log all SQL traffic between applications and Firebird database.

There are 2 ways to do it in Firebird 2.5 and later: FBScanner and Trace API, and the only way in early versions of Firebird: FBScanner.

FBScanner (Firebird Scanner) is a tool that can monitor and view all traffic between Firebird and InterBase servers and their client applications.

FBScanner can log all SQL traffic to text files and external Firebird database, it includes FBScanner LogAnalyzer module to analyze SQL performance.

Why not use Trace API?

For Firebird 2.5 users using TraceAPI could be good solution, but for high-load system TraceAPI can add too much load to Firebird. FBScanner add only 5-10% overhead to general operations and, moreover, it can be installed on separate [debug] server, so users and applications will not see any difference.
Also, as you can see below, FBScanner offers convenient user interface to analyze plans and queries.

FBScanner stores all information into the single table. It uses self-links to reduce the amount of stored information and it makes raw log hard to read and understand.

To facilitate log analysis we have created new module in FBScanner 3.0 - LogAnalyzer.

At first start LogAnalyzer will create necessary indices, it can take several minutes.

After that LogAnalyzer will show the last available day in the log at the “Server Load” tab:

“Server Load” tab shows how many SQL queries were run per minute, and how much time they took to execute. Effectively it shows server load, i.e., number of queries and their execution times.

Zoom in (button in the top left corner of the tab “Server load”), drag graph by holding right-button of the mouse and select the peak you are interested to investigate – click right-button to show popup-menu

It will show you tab “All statements”, where you can browse SQL queries

Select any query to see its text and, if plan logging feature is enabled, its plan.

To follow the execution flow, you can right-click on the query and look for connection and transactions for this query

LogAnalyzer marks bold queries in the same transaction:

You can sort queries and, for example, find query with the longest execution time:

To know more about this query - double-click on it and see more details

To perform it, we can use the following script (to run it immediately after database creation script) :

set stat on; /*show statistics*/

select * from ins1mln;

update tableind1 SET MALEORFEMALE = 3;

delete from tableind1;

select count(*) from tableind1;

and store result for future analysis.

Then we will create database with the same structure - but add index for MALEORFEMAIL column

CREATE INDEX TABLEIND1_IDX1 ON TABLEIND1 (MALEORFEMALE);

As you can see, we insert to this column only 0 or 1 integer values.

Then we repeat the script with this index and compare results.

Results are in the following table:

Without index for MALEORFEMAIL

With index for MALEORFEMALE

SQL> set stat on; /*show statistics*/

SQL> select * from ins1mln;

INSERTED_CNT

============

1000000

Current memory = 10487216

Delta memory = 80560

Max memory = 12569996

Elapsed time= 13.33 sec

Buffers = 2048

Reads = 0

Writes 18756

Fetches = 7833503

SQL> update tableind1 SET MALEORFEMALE = 3;

Current memory = 76551788

Delta memory = 66064572

Max memory = 111442520

Elapsed time= 15.04 sec

Buffers = 2048

Reads = 16166

Writes 15852

Fetches = 6032307

SQL> delete from tableind1;

Current memory = 76550240

Delta memory = -1548

Max memory = 111442520

Elapsed time= 3.27 sec

Buffers = 2048

Reads = 16147

Writes 16006

Fetches = 5032277

SQL> select count(*) from tableind1;

COUNT

============

0

Current memory = 76552064

Delta memory = 1824

Max memory = 111442520

Elapsed time= 1.35 sec

Buffers = 2048

Reads = 16021

Writes 1791

Fetches = 2032278

SQL> set stat on; /*show statistics*/

SQL> select * from ins1mln;

INSERTED_CNT

============

1000000

Current memory = 10484140

Delta memory = 75524

Max memory = 12569996

Elapsed time= 23.94 sec

Buffers = 2048

Reads = 1

Writes 23942

Fetches = 11459599

SQL> update tableind1 SET MALEORFEMALE = 3;

Current memory = 76548712

Delta memory = 66064572

Max memory = 111439444

Elapsed time= 29.30 sec

Buffers = 2048

Reads = 16167

Writes 19492

Fetches = 10035948

SQL> delete from tableind1;

Current memory = 76547164

Delta memory = -1548

Max memory = 111439444

Elapsed time= 3.41 sec

Buffers = 2048

Reads = 16147

Writes 15967

Fetches = 5032277

SQL> select count(*) from tableind1;

COUNT

============

0

Current memory = 76548988

Delta memory = 1824

Max memory = 111439444

Elapsed time= 0.69 sec

Buffers = 2048

Reads = 16021

Writes 1901

Fetches = 2032278

So, bad index decreases performance by approximately 2 times while inserting or updating. Also we can see that non-optimal index greatly increases number of writes and record fetches.

Let’s get statistics for this sample database (with bad index for MALEORFEMALE) and try to find some details.

To gather statistics, we run the following command:

gstat -r e:\testfb25index.fdb > e:\teststat.txt

TABLEIND1 table and indices statistics section looks intriguing, but what useful information it gives to us?

TABLEIND1 (128)

Primary pointer page: 166, Index root page: 167

Average record length: 0.00, total records: 1000000

Average version length: 27.00, total versions: 1000000, max versions: 1

Data pages: 16130, data page slots: 16130, average fill: 93%

Fill distribution:

0 - 19% = 1

20 - 39% = 0

40 - 59% = 0

60 - 79% = 0

80 - 99% = 16129

Index RDB$PRIMARY1 (0)

Depth: 3, leaf buckets: 1463, nodes: 1000000

Average data length: 1.00, total dup: 0, max dup: 0

Fill distribution:

0 - 19% = 0

20 - 39% = 0

40 - 59% = 0

60 - 79% = 1

80 - 99% = 1462

Index TABLEIND1_IDX1 (1)

Depth: 3, leaf buckets: 2873, nodes: 2000000

Average data length: 0.00, total dup: 1999997, max dup: 999999

Fill distribution:

0 - 19% = 0

20 - 39% = 1

40 - 59% = 1056

60 - 79% = 0

80 - 99% = 1816

To understand the meaning of shown numbers and percentage values, we can use IBAnalyst tool, which offers visual interpretation of database statistics:

By clicking Reports/View recommendations we can find the appropriate explanation for this index:

Bad indices count: 1.

By `bad` we name indices with many duplicate keys (90% of all keys)

and big groups of equal keys (30% of all keys). Big groups of equal

keys slowdown garbage collection - MaxEquals here is % of max groups

of keys having equal values. Index search for such an index is not

efficient. You can drop such indices (if they are not on FK

constraints).

Index ( Relation) Duplicates MaxEquals

TABLEIND1_IDX1 (TABLEIND1) : 100%, 50%

In production databases often we can see many bad indices, which can greatly affect database performance. In this example we can see table with 13 millions of records which have 7 bad indices, which are (most likely) useless and greatly decrease Firebird performance.

Usually the first move of developer is to drop these bad indices. On other hand, it’s possible that bad index was used in some particular SQL query, which required specific combination of indices (including bad one) in order to run fast enough.

Firebird SQL optimizer has complex rules, and it often happens that dropping of bad index can lead to significant performance degradation in some queries.

Certainly, these SQL queries should be rewritten to use more optimal execution plan without bad index, but there is a still a problem: how to find SQL queries which use bad index? The answer is in the next episode of our Optimization series of articles.